To quantify the differences among datasets, we first conduct a “Name-The-Dataset” task on the four datasets [1]}, treating each dataset as a domain.
We split the users 80%/20% into training/testing set, and use daily features as the input. We use a portion of users in the training data to train a small Random Forest (RF, n=10, max depth=3) to classify which dataset a data belongs to (i.e., four-class classification).
The left side of Fig. REF  shows the results. With 1/10/100 users (0.2%/2%/20% of the training set), the model can achieve an accuracy of 62.3%/84.2%/91.1%, which indicates that behavior features from different DS have distinguishing distributions.
We also repeated the training with normalized features, as shown in the right side of Fig. REF .
The normalization can reduce the distribution shift, especially for DS1 and DS4, but the distinction between datasets still persists.
